Abstract
In this work, the feasibility of applying a Sliding Mode Predictive Controller (SMPC) to improve greenhouse inside air temperature control is addressed in terms of energy consumption, disturbance handling and set point tracking accuracy. Major research issues addressed concern the SMPC robustness study in greenhouse control, as well as to evaluate if the levels of performance and energy consumptions are acceptable when compared with the traditional generalized predictive controller. Besides the external disturbances related to weather conditions throughout the considered period, such as solar radiation and temperature variations, internal effects of irrigation system and external air flow entering the greenhouse must be taken into account. Simulations based on real data, carried out for a period of 4months, suggest that the strategy herein described not only appropriately rejects these disturbances, but also keeps the manipulated variables (heating and cooling) within feasible practical limits, with low levels of energy consumption, motivating its refinement for real application. SMPC results are presented and compared with the ones obtained with the generalized predictive controller. Both controllers are subject to actuator constraints and employ the Quadratic Programming for optimization. Copyright (c) 2015 John Wiley & Sons, Ltd.

Abstract
A teaching experiment is proposed in which an artificial intelligence technique is blended with classical control techniques to design PID controllers. The artificial intelligence technique deployed is currently considered one of the most popular and successfully nature and biological inspired metaheuristics: the particle swarm optimization algorithm. The teaching experiment is proposed for an introductory undergraduate Biomedical Engineering feedback control systems course. The mean arterial pressure control, quite relevant in practical application terms, is revisited. Moreover, another biomedical control problem is proposed for teaching/learning purposes: the minimum temperature control for intracranial tumor treatment. Simulation results concerning both classic and artificial intelligence based techniques for PID controller design are presented.

Abstract

Abstract
The author's wishes to make the following correction: all the IAE values presented in the paper are multiplied by a factor of 100. The authors would like to apologise for any inconvenience caused. © 2015 Elsevier B.V.

Abstract
The grey wolf optimization algorithm is proposed to design proportional, integrative and derivative controllers using a two degrees of freedom control configuration. The control system is designed in order to achieve good set-point tracking and disturbance rejection performance. The design is accomplished by minimizing an aggregated cost function based on the time-weighted absolute error integral, subjected to robustness constraints. The control system robustness levels are prescribed in terms of the vector margin and maximum complementary sensitivity function values. Simulation results are presented for several common systems dynamics and compared with the ones obtained with a particle swarm optimization algorithm.

Abstract
This paper describes a proposal to develop a Tourism Recommendation System based in users enhanced profiles (composed by basic user information, relations between user and a set of stereotypes and user functionality levels). The main focus of this work is to evaluate if user's physical and psychological functionality levels considered in user's profiles creation, will produce significant changes in the recommendation results. This work aims also to contribute with a different way to classify points-of-interest (POI) considering their capacity to receive tourists with certain levels of physical and psychological issues that will be described in this paper sections. © 2016 ACM.